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AI could turn China’s social credit system into surveillance tool: government-funded study

China’s social credit system, stereotypically imagined as an all-powerful numerical scoring project for authoritarian control, has gained notoriety in the West – although people inside the country actually feel little of its presence or practical influence. In a government-funded study, however, a Chinese legal scholar warned that deploying artificial intelligence (AI) risked turning the rating…

AI could turn China’s social credit system into surveillance tool: government-funded study

A Chinese government-funded study suggests that implementing artificial intelligence (AI) in the country's social credit system could transform it into a powerful surveillance tool. The research, published in the Journal of Sichuan Normal University, warns that using AI to assess citizens' activities through big data and algorithms could lead to issues such as opacity, large-scale surveillance, and discrimination.

Social credit ratings, which translate citizens' activities into quantifiable metrics, are already in use, but the study suggests that the deployment of AI could amplify the risks associated with the system. Lin Jianxing, a doctoral candidate at Sichuan University's Law School, expressed concern that the algorithmic administration could result in a lack of transparency and potential discrimination against individuals.

While a high or low score might not directly impact an individual's rights and obligations, it could affect their treatment in areas such as healthcare, financial lending, and government services. The study highlights the potential risks of relying on AI within the credit system, particularly regarding the reliability of algorithms and the quality of data used to create profiles.

Lin cautioned that without strict data compliance regulations and oversight mechanisms, the mechanism could evolve from a risk management tool into a means of behavioral surveillance.

Written by urgent.news from Reuters Business via SCMP's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

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